Ensemble deep learning for multilabel binary classification of user-generated content

Haralabopoulos, G., Anagnostopoulos, I. and McAuley, D. (2020) Ensemble deep learning for multilabel binary classification of user-generated content. Algorithms, 13 (4). 83. ISSN 1999-4893 doi: 10.3390/a13040083

Abstract/Summary

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5% to 5.4% .

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/105384
Identification Number/DOI	10.3390/a13040083
Refereed	Yes
Divisions	No Reading authors. Back catalogue items Henley Business School > Business Informatics, Systems and Accounting
Publisher	MDPI
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